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Article: Nonlinear system modeling via knot-optimizing B-spline networks

TitleNonlinear system modeling via knot-optimizing B-spline networks
Authors
KeywordsB-Splines
Knot Points
Neural Network
Nonlinear System Modeling
Issue Date2001
Citation
Ieee Transactions On Neural Networks, 2001, v. 12 n. 5, p. 1013-1022 How to Cite?
AbstractIn using the B-spline network for nonlinear system modeling, owing to a lack of suitable theoretical results, it is quite difficult to choose an appropriate set of knot points to achieve a good network structure for minimizing, say, a minimum error criterion. In this paper, a novel knot-optimizing B-spline network is proposed to approximate general nonlinear system behavior. The knot points are considered to be independent variables in the B-spline network and are optimized together with the B-spline expansion coefficients. A simulated annealing algorithm with an appropriate search strategy is used as an optimization algorithm for the training process in order to avoid any possible local minima. Examples involving dynamic systems up to six dimensions in the input space to the network are solved by the proposed method to illustrate the effectiveness of this approach.
Persistent Identifierhttp://hdl.handle.net/10722/155854
ISSN
2011 Impact Factor: 2.952
References

 

DC FieldValueLanguage
dc.contributor.authorYiu, KFCen_US
dc.contributor.authorWang, Sen_US
dc.contributor.authorTeo, KLen_US
dc.contributor.authorTsoi, ACen_US
dc.date.accessioned2012-08-08T08:38:02Z-
dc.date.available2012-08-08T08:38:02Z-
dc.date.issued2001en_US
dc.identifier.citationIeee Transactions On Neural Networks, 2001, v. 12 n. 5, p. 1013-1022en_US
dc.identifier.issn1045-9227en_US
dc.identifier.urihttp://hdl.handle.net/10722/155854-
dc.description.abstractIn using the B-spline network for nonlinear system modeling, owing to a lack of suitable theoretical results, it is quite difficult to choose an appropriate set of knot points to achieve a good network structure for minimizing, say, a minimum error criterion. In this paper, a novel knot-optimizing B-spline network is proposed to approximate general nonlinear system behavior. The knot points are considered to be independent variables in the B-spline network and are optimized together with the B-spline expansion coefficients. A simulated annealing algorithm with an appropriate search strategy is used as an optimization algorithm for the training process in order to avoid any possible local minima. Examples involving dynamic systems up to six dimensions in the input space to the network are solved by the proposed method to illustrate the effectiveness of this approach.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Transactions on Neural Networksen_US
dc.subjectB-Splinesen_US
dc.subjectKnot Pointsen_US
dc.subjectNeural Networken_US
dc.subjectNonlinear System Modelingen_US
dc.titleNonlinear system modeling via knot-optimizing B-spline networksen_US
dc.typeArticleen_US
dc.identifier.emailYiu, KFC:cedric@hkucc.hku.hken_US
dc.identifier.authorityYiu, KFC=rp00206en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/72.950131en_US
dc.identifier.scopuseid_2-s2.0-0035441376en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035441376&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume12en_US
dc.identifier.issue5en_US
dc.identifier.spage1013en_US
dc.identifier.epage1022en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridYiu, KFC=24802813000en_US
dc.identifier.scopusauthoridWang, S=7410335978en_US
dc.identifier.scopusauthoridTeo, KL=35569785000en_US
dc.identifier.scopusauthoridTsoi, AC=7005107318en_US

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